Graphical representation of the effects of antenna locations on path loss data [aircraft EMI]

2004 ◽  
Author(s):  
M. Jafri ◽  
J. Ely ◽  
L. Vahala
Keyword(s):  
Graphical Representation ◽  
Path Loss ◽  
Loss Data

scholarly journals Path loss models with distance‐dependent weighted fitting and estimation of censored path loss data

2016 ◽  
Vol 10 (14) ◽  
pp. 1467-1474 ◽  
Author(s):  
Aki Karttunen ◽  
Carl Gustafson ◽  
Andreas F. Molisch ◽  
Rui Wang ◽  
Sooyoung Hur ◽  
...  
Keyword(s):  
Path Loss ◽  
Loss Data ◽  
Loss Models

scholarly journals Millimeter-Wave Omnidirectional Path Loss Data for Small Cell 5G Channel Modeling

IEEE Access ◽  
2015 ◽  
Vol 3 ◽  
pp. 1573-1580 ◽  
Author(s):  
George R. Maccartney ◽  
Theodore S. Rappaport ◽  
Mathew K. Samimi ◽  
Shu Sun
Keyword(s):  
Millimeter Wave ◽  
Path Loss ◽  
Channel Modeling ◽  
Small Cell ◽  
Loss Data

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

Path Loss Model in Crowded Outdoor Environments Considering Multiple Human Body Shadowing of Multipath at 4.7GHz and 26.4GHz

2019 ◽  
Vol E102.B (8) ◽  
pp. 1676-1688 ◽  
Author(s):  
Mitsuki NAKAMURA ◽  
Motoharu SASAKI ◽  
Wataru YAMADA ◽  
Naoki KITA ◽  
Takeshi ONIZAWA ◽  
...  
Keyword(s):  
Human Body ◽  
Path Loss ◽  
Loss Model ◽  
Path Loss Model ◽  
Outdoor Environments

Path Loss Characterization in a Body-Centric Scenario at 94GHz

2013 ◽  
Vol E96.B (10) ◽  
pp. 2448-2454 ◽  
Author(s):  
Alice PELLEGRINI ◽  
Alessio BRIZZI ◽  
Lianhong ZHANG ◽  
Khaleda ALI ◽  
Yang HAO
Keyword(s):  
Path Loss
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